Currently Viewing:
The American Journal of Managed Care December 2016
Currently Reading
Getting From Here to There: Health IT Needs for Population Health
Joshua R. Vest, PhD, MPH; Christopher A. Harle, PhD; Titus Schleyer, DMD, PhD; Brian E. Dixon, MPA, PhD, FHIMSS; Shaun J. Grannis, MD, MS, FAAFP, FACMI; Paul K. Halverson, DrPH, FACHE; and Nir Menache
How Health Plans Promote Health IT to Improve Behavioral Health Care
Amity E. Quinn, PhD; Sharon Reif, PhD; Brooke Evans, MA, MSW; Timothy B. Creedon, MA; Maureen T. Stewart, PhD; Deborah W. Garnick, ScD; and Constance M. Horgan, ScD
Data-Driven Clinical and Cost Pathways for Chronic Care Delivery
Yiye Zhang, PhD, and Rema Padman, PhD
Accountable Care Organization Hospitals Differ in Health IT Capabilities
Daniel M. Walker, PhD, MPH; Arthur M. Mora, PhD, MHA; and Ann Scheck McAlearney, ScD, MS
Building Health IT Capacity to Improve HIV Infection Health Outcomes
Hannah Rettler, MPH; R. Monina Klevens, DDS, MPH; Gillian Haney, MPH; Liisa Randall, PhD; Alfred DeMaria, MD; and Johanna Goderre, MPH
Telemedicine and the Sharing Economy: The "Uber" for Healthcare
Brian J. Miller, MD, MBA, MPH; Derek W. Moore, JD; and Chester W. Schmidt, Jr, MD
Assessing Electronic Health Record Implementation Challenges Using Item Response Theory
Kitty S. Chan, PhD; Hadi Kharrazi, MD, PhD; Megha A. Parikh, MS; and Eric W. Ford, PhD, MPH
Payer—Provider Patient Registry Utilized in a Behavioral Health Home
Michele Mesiano, MSW; Meghna Parthasarathy, MS; Shari L. Hutchison, MS, PMP; David Salai, BS; Suzanne Daub, LCSW; Mary Doyle, MHIS; and James M. Schuster, MD, MBA
US Hospital Engagement in Core Domains of Interoperability
A. Jay Holmgren, BA; Vaishali Patel, PhD; Dustin Charles, MPH; and Julia Adler-Milstein, PhD

Getting From Here to There: Health IT Needs for Population Health

Joshua R. Vest, PhD, MPH; Christopher A. Harle, PhD; Titus Schleyer, DMD, PhD; Brian E. Dixon, MPA, PhD, FHIMSS; Shaun J. Grannis, MD, MS, FAAFP, FACMI; Paul K. Halverson, DrPH, FACHE; and Nir Menache
Aligning health information technology with population health requires organizations to think differently about data needs, exchange partners, and how to leverage both for evidence-based action.
Broaden Health Information Exchange
In the pre-MU era, health IT and EHRs were data silos and repositories of information that could not easily be shared between care providers. Health information exchange (HIE) was developed to share critical patient information. To date, however, HIEs have minimally supported population health initiatives. For one, social service organizations and public health agencies are rarely partners in an HIE.17 Population health requires collaboration, partnership, and cooperation with social service organizations and public health agencies because healthcare organizations lack the services, programs, or expertise to address many of the determinants of health. Likewise, many healthcare organizations’ HIE activities are narrow in scope. Factors such as limited participation in community-based HIE organizations, the growing use of enterprise HIEs, or single-vendor mediated EHR strategies limit the widespread availability of patient data in a given market. Such strategies make it difficult—if not impossible—to assemble comprehensive patient histories, aggregate data for population health, and coordinate care.18 In addition, even when available through HIEs, information from external providers is rarely integrated into clinical systems,10 resulting in limited ability to leverage exchanged information for clinical decision making.
Avenues to expand organizational participation in HIEs exist. For example, healthcare organizations can identify social service partners (eg, 2-1-1 listed programs) and assist them in connecting to an HIE network or obtaining direct secure messaging accounts. Such arrangements would facilitate patient transitions to service providers capable of addressing a broader set of health determinants. Also, these arrangements could facilitate communication from the social service organizations and supplant the need for healthcare organizations to directly capture social determinants in health IT systems at the point of care.
Similarly, simply considering who has the ability to act upon HIE information could suggest new priority partners. For example, emerging HIE event notification systems alert providers about key patient events like hospitalizations. Although small medical practices may not have the capacity to respond to these events,19 case management and home health agencies have the expertise and staff capable of coordinating care in response. Additionally, partnerships with public health agencies, which often maintain data on geographic populations, could provide access to data on social, behavioral, and environmental factors currently absent from health IT systems.
Lastly, HIE will be most effective if data-sharing partnerships accurately reflect patients’ care patterns within the community. This may require healthcare organizations to consider HIE needs beyond a single vendor and novel approaches for engaging new partners (and even competitors) to the mutual benefit of a given population.
Translating Data to Actionable Information

Today, healthcare organizations typically estimate risk using only clinical and care utilization measures, even though social, behavioral, and environmental factors are also relevant. Thus, prediction models, such as those for hospital readmission, often perform poorly.20 Armed with expanded and widely shared data reflecting the contexts and behaviors that influence health, the next step is to transform these data into actionable information to achieve population health goals. A clear application is to augment current risk stratification approaches, which attempt to divide populations into groups for targeted interventions. New data sources and information-sharing partners may lead to better-performing models, and they may allow us to characterize and predict more population health–relevant outcomes. For example, many existing risk models predict outcomes like death, care costs, or care utilization. Although these are important, they are distal from the basic goals of population health. Instead, data on health behaviors and other social determinants may be a means to predict upstream factors, such as physical function and quality of life, which are more relevant to population health goals of widespread physical, mental, and social well-being.21 Finally, this new information must be put in front of the users and shared with partner organizations so they can take action.
A shift in how healthcare leaders think about data collection, data sharing, and translating data into actionable information is neither insurmountable nor technologically difficult. The capabilities exist to collect, integrate, and analyze large bodies of data relevant to human health, including social, behavioral, public health, and environmental factors. Instead, as healthcare organizations establish population health goals, leaders must ensure their organizations’ data collection and analytic capabilities align with their changing business needs. As organizations become accountable for population health, their leaders will need to initiate collaborations and agreements with nontraditional partners to obtain, share, and use social indicators and service information in order to optimally leverage health IT resources in pursuit of enhanced healthcare and population health. 

Author Affiliations: Richard M. Fairbanks School of Public Health (JRV, CAH, BED, PH, NM), and School of Medicine (TS, SJG), and Center for Health Services and Outcomes Research (BED), Indiana University, Indianapolis, IN; Center for Biomedical Informatics, Regenstrief Institute, Inc (JRV, CAH, TS, BED, SJG, NM), Indianapolis, IN; Center for Health Information and Communication (BED), Department of Veterans Affairs, Indianapolis, IN.

Source of Funding: Support for this publication was provided by the Robert Wood Johnson Foundation through the Systems for Action National Coordinating Center, ID 73485.

Author Disclosures: Dr Schleyer is an employee of the Indiana University School of Medicine, which engages in research and design of health information exchanges (HIEs) through his appointment at the Regenstrief Institute. As a part of his full-time position, he participates in a wide variety of HIE and health IT–related projects. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (BED, SJG, CAH, PKH, NM, TS, JRV); drafting of the manuscript (BED, SJG, CAH, TS, JRV); critical revision of the manuscript for important intellectual content (BED, SJG, CAH, NM, TS, JRV); administrative, technical, or logistic support (BED, CAH, PKH); and supervision (PKH, NM).

Address Correspondence to: Joshua R. Vest, PhD, MPH, Richard M. Fairbanks School of Public Health at Indiana University–Purdue University Indianapolis, 1050 Wishard Blvd, Rm 5124, Indianapolis, IN 46202-2872. E-mail:

1. The Merit-Based Incentive Payment Program System (MIPS). CMS website. Accessed July 6, 2016.

2. Medicare program: Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) incentive under the physician fee schedule, and criteria for physician-focused payment models. Federal Register website. Published May 9, 2016. Accessed November 2016. 

3. Kocher RP, Adashi EY. Hospital readmissions and the Affordable Care Act: paying for coordinated quality care. JAMA. 2011;306(16):1794-1795. doi: 10.1001/jama.2011.1561.

4. Additional requirements for charitable hospitals; community health needs assessments for charitable hospitals; requirement of a section 4959 excise tax return and time for filing the return. Internal Revenue Service website. Published February 2, 2015. Accessed June 16, 2016.

5. Gase LN, Pennotti R, Smith KD. “Health in all policies”: taking stock of emerging practices to incorporate health in decision making in the United States. J Public Health Manag Pract. 2013;19(6):529-540. doi: 10.1097/PHH.0b013e3182980c6e.

6. Lavizzo-Mourey R. Building a culture of health. Robert Wood Johnson Foundation website. Published 2014. Accessed November 2016.

7. McDonald CJ, Overhage JM, Tierney WM, et al. The Regenstrief Medical Record System: a quarter century experience. Int J Med Inform. 1999;54(3):225-253.

8. Richardson JE, Vest JR, Green CM, Kern LM, Kaushal R; HITEC Investigators. A needs assessment of health information technology for improving care coordination in three leading patient-centered medical homes. J Am Med Inform Assoc. 2015;22(4):815-820. doi: 10.1093/jamia/ocu039.

9. Sheikh A, Sood HS, Bates DW. Leveraging health information technology to achieve the “triple aim” of healthcare reform. J Am Med Inform Assoc. 2015;22(4):849-856. doi: 10.1093/jamia/ocv022.

10. Wu FM, Rundall TG, Shortell SM, Bloom JR. Using health information technology to manage a patient population in accountable care organizations. J Health Organ Manag. 2016;30(4):581-596. doi: 10.1108/JHOM-01-2015-0003.

11. Woolf SH, Braveman P. Where health disparities begin: the role of social and economic determinants—and why current policies may make matters worse. Health Aff (Millwood). 2011;30(10):1852-1859. doi: 10.1377/hlthaff.2011.0685.

12. Calvillo-King L, Arnold D, Eubank KJ, Lo M, Yunyongying P, Stieglitz H, Halm EA. Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review. J Gen Intern Med. 2013;28(2):269-282. doi: 10.1007/s11606-012-2235-x.

13. Rosen AK, Reid R, Broemeling AM, Rakovski CC. Applying a risk-adjustment framework to primary care: can we improve on existing measures? Ann Fam Med. 2003;1(1):44-51.

14. Ask J. Where would the wearables market be without smartphones? Forrester website. Published September 28, 2015. Accessed June 20, 2016.

15. Comstock J. Survey: 40 percent of doctors say they have recommended wearables, only 4 percent of patients say they have. Mobihealthnews website. Published June 20, 2016. Accessed June 20, 2016.

16. Comer KF, Grannis S, Dixon BE, Bodenhamer DJ, Wiehe SE. Incorporating geospatial capacity within clinical data systems to address social determinants of health. Public Health Rep. 2011;126(suppl 3):54-61.

17. Nguyen OK, Chan CV, Makam A, Stieglitz H, Amarasingham R. Envisioning a social-health information exchange as a platform to support a patient-centered medical neighborhood: a feasibility study. J Gen Intern Med. 2015;30(1):60-67. doi: 10.1007/s11606-014-2969-8.

18. Vest JR, Kash BA. Differing strategies to meet information sharing needs: the publicly supported community health information exchange versus health systems’ enterprise health information exchanges. Milbank Q. 2016;94(1):77-108. doi: 10.1111/1468-0009.12180.

19. Vest JR, Ancker JS. Health information exchange in the wild: the association between organizational capability and perceived utility of clinical event notifications in ambulatory and community care [published online April 23, 2016]. J Am Med Inform Assoc. pii: ocw040. doi: 10.1093/jamia/ocw040.

20. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688-1698. doi: 10.1001/jama.2011.1515.

21. Haas LR, Takahashi PY, Shah ND, et al. Risk-stratification methods for identifying patients for care coordination. Am J Manag Care. 2013;19(9):725-732. 
Copyright AJMC 2006-2018 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up

Sign In

Not a member? Sign up now!